Microsoft SQL Server 2008 R2 Unleashed- P211 pptx

FIGURE 51.35 The Comp Sales cube definition in the cube designer.. To do so, you right-click the Comp Sales cube entry in the Solution Explorer and choose the Process item or choose the

FIGURE 51.35 The Comp Sales cube definition in the cube designer

Building and Deploying the Cube

You basically have a cube definition now, but it is just an empty shell You need to process it

and then deploy it so that it is instantiated and populated with data (via the data source view)

Remember that this cube definition is a solution project, just like a C# code project It must be

deployed before it can be used First, you need to verify that the properties of the cube you are

building are set correctly You must have these properties correct before the cube can be

processed (Process, in this case, means build the cube structure and populate the measures and

their associated dimensions.) You can assume that the properties will not be set correctly, so

you should take a quick look and update them accordingly You start by going to the Project

menu item in Visual Studio and locating the Properties item entry (see Figure 51.36)

After you select this option, you navigate to the Deployment entry (the configuration property

on the bottom) You need to focus on the Target (the target of the deployment) properties As

you can see in Figure 51.37, the Serverproperty should be pointing to the location where you

want this cube to be deployed The Databaseproperty is simply the name under which you

will deploy the database For this example, you should make sure to specify a valid Server

value; the default is (localhost) The default in this property usually is not what you want to

happen and usually results in an error during the deployment step Therefore, you should

specify this value explicitly (such as DBARCH-LT2\SQL08DE01, which is the Analysis Services

server, and CompSalesUnleashedas the Databaseentry) After the cube is deployed, you will be

able to connect to this server (SSAS engine) with SSMS and administer the cube accordingly

After you apply these property changes, you are ready to first do a build and then deploy

your SSAS cube You start by making sure you have a successful build by using the Build

menu item on the toolbar or using the specific build option for the current SSAS solution:

Build CompSalesUnleashed They both do the same thing If you have no errors (and you

have received a Build Succeeded message in the lower-left message bar of Visual Studio),

you can deploy this SSAS solution

FIGURE 51.36 Selecting the cube properties for Comp Sales from the Project menu

FIGURE 51.37 Deployment properties for the Comp Sales cube

Again, you should choose the Build menu item in the toolbar and click the Deploy

Solution option to deploy this cube Immediately, a Deployment Progress dialog box

appears in the lower-right corner of Visual Studio When the deployment has progressed,

you receive a Deployment Completed Successfully message

Populating the Cube with Data

Now you can process actual data into your cube from the data source view To do so, you

right-click the Comp Sales cube entry in the Solution Explorer and choose the Process

item or choose the Process icon for the cube in the cube designer (second icon from the

left in the cube designer) A Process Cube dialog appears, with the object list of available

cubes to process You select the Comp Sales cube (by highlighting it) and then click the

Run button to start the processing of data (see Figure 51.38) You can also see in Figure

51.38 that the Process Option defaults to Process Full Other options here vary depending

on what part of the cube needs to be reprocessed (such as when you have structure

changes, data refreshes, incremental data changes, so on)

A Process Progress dialog appears as the processing begins Remember that this data is the

dimension member values and the measure data values and has not been aggregated up

through a complete cube representation (at all levels in the hierarchies) That will be done

shortly, via the Aggregation Design Wizard You can actually use your cube right now, but

browsing would be challenging from a performance point of view

Aggregating Data Within the Cube

The last step of creating your OLAP cube is running through the Aggregation Design

Wizard and determining how best to represent and aggregate the data for your users This

is point at which you must determine the optimal aggregation levels and storage method

for these aggregations (MOLAP, HOLAP, or ROLAP) for the optimal performance of queries

against the cube

You double-click the cube entry in the Solution Explorer (Comp Sales.cube) to bring up

the cube designer for your newly created cube Then you click the Partitions tab to see the

current partition for Comp Sales Figure 51.39 shows the default storage mode is MOLAP

FIGURE 51.38 Process Cube dialog for Comp Sales

and that there is no Aggregation Design for this cube yet Just to the lower right of this

tab is the Storage Settings option, which shows the different storage options possible for

your partition, as shown in Figure 51.40

You need to indicate what type of storage mode and caching options you want for the

partition that will contain your aggregations (these storage modes are discussed earlier in

this chapter) You want to optimize performance and don’t need real-time refreshes of the

data For these reasons, you specify the MOLAP (native SSAS storage) mode Figure 51.40

FIGURE 51.39 The Partitions for the Comp Sales cube

FIGURE 51.40 Specifying MOLAP storage mode for your cube in the Storage Settings dialog

shows this MOLAP specification in the Storage Settings dialog This dialog works as a sliding

scale You just need to make sure the slider is positioned at the MOLAP storage option

You also want to take advantage of the proactive caching capabilities that come with SSAS

You can activate this feature by clicking the Options button of this dialog and then

check-ing the Enable Proactive Cachcheck-ing check box at the top of the Storage Options dialog that

appears (see Figure 51.41) In addition, you use the option Update the Cache When Data

Changes, as indicated in Figure 51.41 along with interval times for these refreshes

A good rule of thumb is to refresh the cache interval based on response requirements and

the volatility of the data from the data source views and whether the changes will have a

dramatic effect on the BI query results

Now you can run through the Aggregation Design Wizard to see whether you can

opti-mize your partition for querying You simply go to the Aggregation Design tab for this

cube (from the cube designer) and choose the Design Aggregations option (click the first

icon in the Design Aggregations tab or right-click within the Aggregation Design tab and

choose Design Aggregations) This launches the Design Aggregation Wizard

FIGURE 51.41 Enabling proactive caching for the cube

First up is the dialog that allows you to specify object counts of the total population of

facts and the number of values at each hierarchical level within each dimension If you

know what the full extent of counts will be for your cube, you can manually supply these

count values in the Estimated Count column (see Figure 51.42) You typically do this

when you have been able to load only a partial amount of data or the data will grow quite

rapidly over time If you are building a statically sized cube and have populated the data

already, you just click the Count button to tell the wizard to use the actual data as the

basis of the aggregation

The next dialog optimizes the storage, based on the level of aggregation You can specify a

maximum storage approach (you create optimized storage based on the amount of disk

space you can allocate to the cube), tell the wizard to simply optimize to achieve a certain

percentage of performance gain (for example, 50%, 80%), specify to start the aggregation

design process dynamically, and stop when you feel the cube is optimized enough, or do

no design aggregation at all You really want to see the design aggregation process happen

Remember that the higher the performance you want, the more storage it will require

(and the longer it will take to reprocess the aggregations) As you can see in Figure 51.43,

you should select the I Click Stop option and stop the design aggregation when the

opti-mization level starts to level off (somewhere between 75% to 88% optiopti-mization level) Any

further optimization would really just waste storage space

FIGURE 51.42 Specifying cube object counts for aggregation in the Aggregation Design

Wizard

When you are satisfied with the aggregation design, you simply click NEXT and name this

design (the sample is named AggregationDesignPrimary, as you can see in Figure 51.44)

You then assign this design aggregation to the partition to use in the Partition tab

If your company has sales transaction data for the past five years and 250 stores that sell

an average of 1,000 items per day, the fact table will have 456,500,000 rows This is

obvi-ously a challenge in terms of disk space by itself, without aggregation tables to go along

with it The control that SSAS provides here is important in balancing storage and retrieval

speed (that is, performance versus size) Aggregations are built to optimize rollup

opera-FIGURE 51.43 Setting the optimal storage and query performance level in the Aggregation

Design Wizard

FIGURE 51.44 Resulting aggregation design to be assigned to the Comp Sales Factoid

parti-tion

tions so that higher levels of aggregation are easily derived from the existing aggregations

to satisfy broader queries If a high degree of query optimization weren’t possible due to

limitations in storage space, SSAS might choose to build aggregates of monthly or

quar-terly data only If a user queried the cube for yearly or multiyear data, those aggregations

would be created dynamically from the highest level of pre-aggregated data With disk

storage becoming more and more inexpensive and servers becoming more powerful, the

tendency is to opt for meeting performance gains A recommended approach is to specify

between an 80% and 90% performance gain here

You are now ready to complete the Aggregation Design Wizard The final step is to either

process this aggregation or save your results and process it later You should choose to

process this aggregation now and then click Finish (see Figure 51.45) The Process Progress

dialog appears immediately, and you get to watch the full extent of the cube’s aggregation

partitions being built (that is, populated) Aggregation SQL queries are actually created

under the covers to populate all these aggregation levels (which are implementing your

design levels) It’s nice to have Microsoft dynamically create these complex queries for this

critical performance optimization step so you don’t have to worry about it yourself

When this step completes, you have a fully optimized cube that is ready for data

brows-ing Congratulations!

Browsing Data in the Cube

You’re ready to browse some cube data now There are several ways to view data in a

multidimensional cube OLE DB for OLAP and ADO MD expose interfaces to do this kind

FIGURE 51.45 Deploy and process the aggregation now to complete your cube

of data browsing, and many leading vendors have used these interfaces to build front-end

analysis tools and ActiveX controls These tools should prove useful for developers of user

interfaces in data warehousing and data mart projects You can also easily browse a cube’s

data from either Visual Studio or SSMS or via any tool or facility that uses the

multidimen-sional extensions of SQL (that is, SQL with DMX and MDX extensions)

To browse your newly created cube from SSMS, you fire up SSMS and connect to the SSAS

server (Analysis Services server type) on which you deployed your cube You should not

connect to the SQL Server Database Engine These are two completely different servers

When you are connected, you expand the Databasestree on the left until you can see the

cube you created (Comp Sales, in this example)

NOTE

In Visual Studio, you can simply click the Browse tab when you are in the cube

design-er All browse functionality uses the same plug-ins, whether you are in Visual Studio or

SSMS In either Visual Studio or SSMS, you can browse the cube (the entire cube with

all dimensions) or just a dimension (using the dimension browser)

In SSMS, you just right-click the Comp Sales cube entry and choose the Browse option As

you can see in Figure 51.46, a multipaned, drag-and-drop interface is your view into the

data in your cube

FIGURE 51.46 Browsing data in your cube in the SMSS data browser

The middle pane lists all cube objects that you can drag into the data browsing pane (on

the right) The data browser uses the Pivot Table Service to access and display your cube’s

data You can expand any of the cube hierarchy objects and see the actual member entries

that are in your cube for each level This capability is helpful when you want to further

filter data in the browser (for example, focus on a particular SKU value or a particular

geography, such as United States or France)

The data browsing pane is easy to use For example, say that you simply want to see all

product sales and product returns for SKUs across all geographies, for each year in the

cube To do this, you expand the measures object until you see all the measures in the

Comp Sales cube Then you drag Sales Units to the center of the lower portion of the data

browsing pane (into the Drop Totals or Detail Fields Here section in the lower right) You

do the same for the Sales Returns measure Data values (totals) for these measures are

already displayed immediately These are the total (aggregated) values for sales returns and

sales units across all products, all geographies, and all times To see the product

break-down of these data measures, you drag the SKU object within the product dimension

object to the Drop Column Fields Here section (just above where the data measures were

dropped) You immediately see the data measure values being broken out by each product

SKU value Now, you drag the Year Time object within the time dimension to the Drop

Row Fields Here section (just to the left of where the data measures were dropped) You

now see the data broken out by the years along the left side (rows) in the cube that

contains sales and return data for products, as shown in Figure 51.47

FIGURE 51.47 Sales units and sales returns for all SKUs by years in the SMSS data browser